System Analysis and Operation Optimization for Aerospace and Related Manufacturing Processes

加拿大航空航天制造业的重要性无论如何都不能高估。根据2013年加拿大航空航天工业协会（AIAC）和加拿大工业新闻稿，加拿大航空航天工业对国家经济的整体影响，占GDP总值的份额，研发活动，就业人数和质量，以及许多其他关键指标，与该国其他制造业相比，都排名很高，如果不是最高的话。先进的制造和组装技术，包括复合材料的制造和使用、精密加工、机器人和自动化等，在很大程度上为加拿大航空航天工业的成功做出了贡献。此外，各种系统工程方法和运营管理工具，如精益工程、供应链管理、生产计划、库存控制、质量控制和改进、制造单元配置等，在航空航天制造工艺的设计和改进中应该而且可以发挥更重要的作用。目前的文献和制造实践表明，有必要进一步开发这些系统工程工具，以解决不同的制造问题，具体到航空航天制造。例如，航空航天质量管理体系AS 9100已被许多大型航空航天公司采用。然而，一些统计质量工具最初是为大规模生产系统开发的，如汽车产品和消费品。另一方面，航空航天制造通常与低生产率、大产品种类、非常高的质量要求和高生产成本相关联。标准统计工具和其他分析工具必须进一步开发或修订，才能适当用于航空航天过程控制和优化。在拟议的研究中，我们将调查和开发统计质量工具，用于航空航天制造业中数据可用性有限且不断发展的应用。此外，我们计划开发新的数学模型和解决方法，或修改现有的，考虑到联合产品开发，利润和风险共享，以及高水平的质量要求，航空航天供应链管理。我们还将研究与航空航天制造业的最佳生产计划和控制有关的问题。一个示例性问题是结合了广泛存在于航空航天和其他制造业中的繁重的手动操作和机器人装配过程的制造系统的生产控制。拟议的研究成果将是建模和解决方案的方法，适用于解决系统工程和优化问题，许多航空航天公司面临的供应链设计和管理，全面质量控制和改进，并在最佳制造系统和工艺开发。研究成果应有利于加拿大航空航天工业与其他发达国家/地区以及新兴经济体的航空航天工业竞争。

The importance of Canada's aerospace manufacturing sector cannot be overestimated in any terms. According to the 2013 Aerospace Industries Association of Canada (AIAC) and Industry Canada News Release, the overall impact of Canadian aerospace industry to the nation's economy, share of the total GDP value, R&D activities, employment numbers and quality, among many other key indicators, are all ranked very high, if not the top ones, in comparing to other manufacturing sectors in the country. Advanced fabrication and assembly technologies including the making and use of composite materials, precision machining, robotics and automation, among many others, have contributed to large extent to the success of Canadian aerospace industry. In addition, various system engineering methodologies and operation management tools such as lean engineering, supply chain management, production planning, inventory control, quality control and improvement, manufacturing cell configuration, etc., should and could play more significant roles in the design and improvement of aerospace manufacturing processes. The current literature and manufacturing practice show that there is a need to further develop these system engineering tools in addressing different manufacturing issues specific to aerospace manufacturing. For example, the aerospace quality management system, AS9100, has been adopted by many major aerospace companies. However, some of the statistical quality tools were initially developed for mass production systems such as those for automotive products and consumer goods. On the other hand, aerospace manufacturing is typically associated with slow production rate, large product variety, very high level quality requirements and high production cost. Standard statistical tools and other analytical tools must be further developed or revised before they can be properly used for aerospace process control and optimization. In the proposed research, we will investigate and develop statistical quality tools for applications in aerospace manufacturing where data availability is limited and evolving. In addition, we plan to develop new mathematical models and solution methods, or modify existing ones, for aerospace supply chain management considering joint product development, profit and risk sharing, and high level quality requirements. We will also investigate problems related to optimal production planning and control of aerospace manufacturing. An example problem is production control of manufacturing systems combining heavy manual operations and robotic assembly processes which widely exist in aerospace and other manufacturing industry. The outcome of the proposed research will be modeling and solution methodologies suitable for solving systems engineering and optimization problems many aerospace companies face in supply chain design and management, total quality control and improvement, and in optimal manufacturing system and process development. The research outcome should benefit Canadian aerospace industry in competing with those from other developed countries/regions as well as from emerging economies.